Methods, systems, articles of manufacture, and apparatus for adaptive metering

ABSTRACT

Methods, systems, articles of manufacture, and apparatus for adaptive metering are disclosed. An example apparatus disclosed herein includes a condition analyzer to determine a condition associated with a mobile device, a meter selector to select a meter for the mobile device based on the condition, and a data collector to collect data pertaining to the mobile device based on the selected meter.

FIELD OF THE DISCLOSURE

This disclosure relates generally to data collection and, moreparticularly, to methods, systems, articles of manufacture, andapparatus for adaptive metering.

BACKGROUND

Media content can be delivered to and presented by a wide variety ofcontent presentation devices such as desktop computers, laptopcomputers, tablet computers, personal digital assistants, smartphones,etc. Because a significant portion of media content is presented to suchdevices, monitoring of media content can provide valuable information toadvertisers, content providers, and the like.

SUMMARY

An example apparatus disclosed herein includes a condition analyzer todetermine a condition associated with a mobile device, a meter selectorto select a meter for the mobile device based on the condition, and adata collector to collect data pertaining to the mobile device based onthe selected meter.

An example apparatus disclosed herein includes a memory storinginstructions and a processor to execute the instructions to determine acondition associated with a mobile device, select a meter for the mobiledevice based on the condition, and collect data pertaining to the mobiledevice based on the selected meter.

An example non-transitory computer readable medium disclosed hereinincludes instructions that, when executed, cause at least one processorto determine a condition associated with a mobile device, select a meterfor the mobile device based on the condition, and collect datapertaining to the mobile device based on the selected meter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment in which examples disclosedherein can be implemented.

FIG. 2 is a block diagram of an example adaptive metering controller inaccordance with teachings of this disclosure.

FIG. 3 is a block diagram of a first example process flow that can beimplemented in examples disclosed herein.

FIG. 4A is a block diagram of a second example process flow that can beimplemented by the example adaptive metering controller of FIG. 2utilizing an accessibility service.

FIG. 4B is a block diagram of a third example process flow that can beimplemented by the example adaptive metering controller of FIG. 2utilizing intent filters.

FIG. 5A is a block diagram of a fourth example process flow that can beimplemented by the example adaptive metering controller of FIG. 2utilizing custom firmware.

FIG. 5B is a block diagram of a fifth example process flow that can beimplemented by the example adaptive metering controller of FIG. 2utilizing one or more external devices.

FIG. 6A is a block diagram of a sixth example process flow that can beimplemented by the example adaptive metering controller of FIG. 2utilizing network traffic.

FIG. 6B is a block diagram of a seventh example process flow that can beimplemented by the example adaptive metering controller of FIG. 2utilizing user-generated inputs.

FIG. 7 is a block diagram of an eighth example process flow that can beimplemented by the example adaptive metering controller of FIG. 2 toprocess call and/or SMS data.

FIG. 8 is a block diagram of a ninth example process flow that can beimplemented by the example adaptive metering controller of FIG. 2 toprocess media event data associated with an application of a mobiledevice.

FIG. 9 is a flowchart representative of machine readable instructionswhich may be executed to implement examples disclosed herein.

FIG. 10 is a block diagram of an example processing platform structuredto execute the instructions of FIG. 7 to implement the example adaptivemetering controller of FIG. 2.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Unless specifically stated otherwise, descriptors such as “first,”“second,” “third,” etc. are used herein without imputing or otherwiseindicating any meaning of priority, physical order, arrangement in alist, and/or ordering in any way, but are merely used as labels and/orarbitrary names to distinguish elements for ease of understanding thedisclosed examples. In some examples, the descriptor “first” may be usedto refer to an element in the detailed description, while the sameelement may be referred to in a claim with a different descriptor suchas “second” or “third.” In such instances, it should be understood thatsuch descriptors are used merely for identifying those elementsdistinctly that might, for example, otherwise share a same name.

DETAILED DESCRIPTION

Adaptive metering is disclosed. In many cases, media content presentedon a mobile device can be monitored using one or more meters implementedon the mobile device. In some cases, data collected using each of themeters may be different, and some or all of the data collected for oneof the meters may overlap with data collected for different ones of themeters. Furthermore, ones of the meters can become unavailable, or canprovide incomplete and/or inaccurate data. As such, the meters used formonitoring media content can be selected to ensure that a desiredaccuracy and/or type of data is collected.

In examples disclosed herein, an example adaptive metering controller isconfigured to select between one or more meters for collecting dataassociated with a mobile device. In some examples, each of the meterscollects the data based on a different number and/or type of inputs. Inexamples disclosed herein, the adaptive metering controller determines acondition associated with the mobile device, where the conditionincludes at least one of a desired accuracy of the data and/or one ormore panels to which a user of the mobile device belongs. In someexamples, based on the condition, the adaptive metering controllerselects a meter and/or a combination of meters, and collects input datafrom the mobile device using the selected meter or combination ofmeters. In some examples, the input data is associated with mediapresented via an application on the mobile device. In some examples, theadaptive metering controller can obtain output data directly from theinput data. The output data may include information associated with thepresented media such as a title, episode, start time, end time, audiolanguage, etc. In other examples, the adaptive metering controllerselects and/or generates a mapping to map the input data to the outputdata.

Advantageously, examples disclosed herein enable the adaptive meteringcontroller to adaptively configure the number and/or type of meters usedfor collecting data. For example, the adaptive metering controller cancollect data using a first meter, then switch to a second meterdifferent from the first meter in response to the first meter becomingunavailable and/or providing inaccurate data. As such, examplesdisclosed herein enable continuous (e.g., without interruption)monitoring of media content on the mobile device while ensuring that thecollected data satisfies a threshold accuracy.

FIG. 1 illustrates an example environment 100 in which examplesdisclosed herein can be implemented. The example environment 100supports monitoring of media presented at one or more monitored sites,such as an example monitored site 102 illustrated in FIG. 1, andincludes example media devices (e.g., a media presentation devices) 104.Although the example of FIG. 1 illustrates one of the monitored site 102and five of the media devices 104, examples disclosed herein can beimplemented in environments 100 supporting any number of monitored sites102 having any number of the media devices 104. Further, examplesdisclosed herein can be implemented in any appropriate networkconfiguration and/or topology.

The environment 100 of the illustrated example includes an exampleadaptive metering controller 106 to monitor media presented by the mediadevices 104. In the illustrated example, the media monitored by theadaptive metering controller 106 can correspond to any type of mediapresentable by the media devices 104. For example, monitored media cancorrespond to media content, such as television programs, radioprograms, movies, Internet video, video-on-demand, etc., as well ascommercials, advertisements, etc. In this example, the adaptive meteringcontroller 106 determines metering data that may identify and/or be usedto identify media presented by the media devices 104 (and, thus, infermedia exposure) at the monitored site 102. The adaptive meteringcontroller 106 then stores and reports this metering data via an examplenetwork 108 to an example data processing facility 110.

In this example, the data processing facility 110 performs anyappropriate post-processing of the metering data to, for example,determine audience ratings information, identify targeted advertising tobe provided to the monitored site 102, etc. In this example, the dataprocessing facilities includes example servers 112 and an examplecentral database 114. In some examples, the post-processing of themetering data is performed on one or more of the servers 112. In someexamples, the central database 114 can store the metering data from theadaptive metering controller 106 and/or processed metering data from theservers 112. In the illustrated example, the network 108 can correspondto any type(s) and/or number of wired and/or wireless data networks, orany combination thereof.

In the illustrated example, each of the media devices 104 monitored bythe adaptive metering controller 106 can correspond to any type ofaudio, video and/or multimedia presentation device capable of presentingmedia audibly and/or visually. For example, each of the media devices104 can correspond to a multimedia computer system, a personal digitalassistant, a cellular/mobile smartphone, a radio, a tablet computer,etc.

In examples disclosed herein, the adaptive metering controller 106 canbe implemented by or otherwise included in each of the media devices104. This example implementation can be especially useful in scenariosin which a media monitoring application is executed on the media devices104, but the media devices 104 prevents (e.g., via digital rightsmanagement or other techniques) third-party applications, such as themedia monitoring application, from accessing protected media data storedon the media device 104.

FIG. 2 is a block diagram of the example adaptive metering controller106 of FIG. 1. In examples disclosed herein, the adaptive meteringcontroller 106 can be implemented in the media device 104 of FIG. 1, andthe adaptive metering controller 106 is configured to select between oneor more meters for collecting metering data associated with the mediadevice 104. In the illustrated example of FIG. 2, the adaptive meteringcontroller 106 includes an example condition analyzer 204, an examplemeter selector 206, an example data collector 208, an example mappingcontroller 210, an example data processor 212, and an example meteradjuster 214. In this example, the adaptive metering controller 106 iscommunicatively coupled to the example network 108 and/or the mediadevice 104 of FIG. 1 via an example data interface 216. Furthermore, theadaptive metering controller 106 is communicatively coupled to anexample database 218. In some examples, the database 218 is implementedon one or more of the media devices 104.

In this example, the condition analyzer 204 determines a conditionassociated with the media device 104. In some examples, the conditionincludes a desired accuracy of the metering data collected from themedia device 104. In some such examples, accuracy of the metering datais based on a number and/or types of meters available for the mediadevice 104. Additionally or alternatively, the condition corresponds toone or more panels to which a user of the media device 104 belongs. Insuch examples, the condition analyzer 204 identifies the one or morepanels, and further identifies a type of output data required by each ofthe one or more panels.

The meter selector 206 of the illustrated example selects one or moremeters for the media device 104 based on the condition. For example,when the condition corresponds to a desired accuracy level of themetering data, the meter selector 206 selects the type and/or number ofthe meters that satisfies a threshold, where the threshold is based onthe desired accuracy level. In other examples, the meter selector 206selects the one or more meters based on the type of output data requiredby each of the one or more panels associated with the media device 104.In some examples, the meter selector 206 selects the one or meters basedon a lack of data (e.g., incomplete data, inconsistent data, etc.).Additionally or alternatively, the meter selector 206 selects the one ormore meters based on a triggering event (e.g., an opening of anapplication, initiating content streaming, selecting a menu option,etc.).

The example data collector 208 collects the metering data pertaining tothe media device 104 based on the one or more selected meters. Forexample, the data collector 208 can collect the metering data using atleast one of an accessibility service, intent filters, firmware, one ormore external devices associated with the mobile device, user-generatedinputs, or network traffic. In some examples, the data collector 208collects the metering data from the media device 104 via the datainterface 216.

The mapping controller 210 of the illustrated example generates amapping to map the metering data to one or more outputs (e.g., events).For example, the mapping controller 210 generates the mapping based onthe metering data collected by the data collector 208 and the type ofoutput data required. In some examples, the mapping includes at leastone of a machine learning model or a neural network model. In someexamples, the mapping controller 210 selects the mapping from one ormore generated mappings previously generated by the mapping controller210. In some such examples, the generated mappings can be stored in thedatabase 218. In some examples, the mapping controller 210 maps themetering data to the one or more outputs, and/or the mapping controller210 provides the selected and/or generated mapping to the data processor212 to perform the mapping.

The example data processor 212 organizes and/or processes the meteringdata. For example, the data processor 212 can use the mapping providedby the mapping controller 210 to generate the output data and/or eventsbased on the metering data. In other examples, based on the type ofmetering data being collected, the data processor 212 can directlyobtain the output data from the metering data (e.g., without themapping). In some examples, the data processor 212 can provide theoutput data and/or the metering data to the database 218 and/or to thecentral database 114 of FIG. 1 via the data interface 216.

The example meter adjuster 214 adjusts and/or modifies the one or moremeters associated with the media device 104. For example, in response tothe metering data not satisfying the condition (e.g., not satisfying athreshold accuracy), the meter adjuster 214 can select a new meter foruse by the data collector 208. In some examples, the meter adjuster 214can remove and/or inactivate one or more of the meters in response tothe meters becoming unavailable and/or providing inaccurate data. Inother examples, the meter adjuster 214 can add one or more new meters inresponse to new input data from the media device 104 becoming available.In some such examples, the meter adjuster 214 can add the one or morenew meters in response to a new external device (e.g., camera,microphone, etc.) being connected to the media device 104. In some otherexamples, the meter adjuster 214 can adjust and/or vary settings ofselected meters (e.g., to increase an accuracy thereof).

In this example, the database 218 stores metering data and/or processedmetering data utilized and/or generated by the adaptive meteringcontroller 106. The example database 218 of FIG. 2 is implemented by anymemory, storage device and/or storage disc for storing data such as, forexample, flash memory, magnetic media, optical media, solid statememory, hard drive(s), thumb drive(s), etc. Furthermore, the data storedin the example database 418 may be in any data format such as, forexample, binary data, comma delimited data, tab delimited data,structured query language (SQL) structures, etc. While, in theillustrated example, the example database 218 is illustrated as a singledevice, the example database 218 and/or any other data storage devicesdescribed herein may be implemented by any number and/or type(s) ofmemories.

FIG. 3 is a block diagram of a first example process flow 300 that canbe implemented in examples disclosed herein. The first process flow 300can be executed by the adaptive metering controller 106 of FIGS. 1and/or 2 to collect data from one or more example inputs 302 associatedwith the media device 104 of FIG. 1, and map the inputs 302 to exampleoutputs (e.g., events) 304 using an example mapping 306. In thisexample, the outputs 304 are associated with media presented via anapplication on the media device 104.

The example inputs 302 can include at least one of image data, audiodata, video data, data from an accessibility service, data from intentfilters, data from memory and/or storage of the media device 104, datafrom user-generated inputs, data from custom firmware, data from one ormore external devices, or network data. In some examples, the inputs 302are accessible to the adaptive metering controller 106 and based on thenumber and/or type of meters selected by the adaptive meteringcontroller 106.

In the illustrated example, the adaptive metering controller 106 selectsthe mapping 306 based on whether data is available from a third-partyapplication on which the media is being presented. In this example, theadaptive metering controller 106 selects an example first mapping 306Awhen data is available from the third-party application, and selects anexample second mapping 306B when data is not available from thethird-party application. In this example, in response to the firstmapping 306A being selected, the adaptive metering controller 106 canuse one or more of the inputs 302 that are available using the one ormore selected meters. In such examples, the adaptive metering controller106 maps the available inputs 302 to desired ones of the outputs 304using the first mapping 306A. Alternatively, in response to the secondmapping 306B being selected, the adaptive metering controller 106 uses asubset of the inputs 302, where the subset corresponds to data that canbe collected without the third-party application. In some examples, thesubset of the inputs 302 includes data from the external devices and/orthe network traffic. In such examples, the adaptive metering controller106 maps the subset of the inputs 302 to the desired ones of the outputs304 using the second mapping 306B.

According to the illustrated example, the outputs 304 include examplemetadata 304A and example state information 304B associated with themedia presented via the application. In some examples, the metadata 304Acan include information pertaining to a user of the media device 104. Insome examples, the media corresponds to a television show, such that themetadata 304A corresponds to a show, season, episode, start time, endtime, media type, audio language, codec, and/or player associated withthe presented media. In this example, the state information 304B isdetermined based on the user interacting with the media. For example,the user can play, pause, seek, stop, resume, and/or end the media basedon user-generated inputs, where the user-generated inputs can includepressing a button, giving an audio command, physical contact with aportion of the media device 104, etc. In some such examples, the stateinformation 304B can include timestamps corresponding to each of theuser-generated inputs.

FIG. 4A is a block diagram of a second example process flow 400 that canbe implemented by the example adaptive metering controller 106 of FIG. 2utilizing an accessibility service. In this example, the meter selector206 of FIG. 2 selects a first meter for collect data pertaining to themedia device 104 of FIG. 1, where the first meter utilizes theaccessibility service of the media device 104.

In examples disclosed herein, the accessibility service is installed onthe media device 104 to assist users with disabilities in using themedia device 104. In some examples, a user of the media device 104 canconfigure the accessibility service to enable the adaptive meteringcontroller 106 to access to data from an application (e.g., third partyapplication) of the media device 104. For example, the accessibilityservice can be configured to enable the adaptive metering controller 106to access example accessibility data 402 using the first meter.

In this example, the data collector 208 of FIG. 2 collects theaccessibility data 402 based on the first meter. The accessibility data402 is based on accessibility events monitored by the accessibilityservice, where each of the accessibility events indicates a statetransition in a user interface of the application. For example, anaccessibility event may correspond to the user clicking a button in theapplication. In this example, the accessibility data 402 can include atleast one of the application, an event type, event text, an event class,a view ID, source text, content description, device time, or an eventtime corresponding to each of the accessibility events.

In the illustrated example, the mapping controller 210 of FIG. 2 canselect an example first mapping 404 to map the accessibility data 402 tothe example outputs 304. For example, the mapping controller 210 cangenerate the metadata 304A and the state information 304B based on theaccessibility data 402 and the first mapping 404. In some examples, themetadata 304A and the state information 304B can be stored in thedatabase 218 of FIG. 2, or sent to the data processing facility 110 ofFIG. 1 via the network 108 of FIG. 1.

FIG. 4B is a block diagram of a third example process flow 406 that canbe implemented by the example adaptive metering controller 106 of FIG. 2utilizing intent filters. In this example, the meter selector 206 ofFIG. 2 selects a second meter for collecting data pertaining to themedia device 104 of FIG. 1, where the second meter utilizes the intentfilters of the media device 104.

In examples disclosed herein, intents can be utilized in the mediadevice 104 to start an activity in an application, where the activitycan include presenting media in the application. In some examples, theintents include data associated with the presented media, and intentfilters can be configured to specify a type of intent that can bereceived by the application. In this example, the second meter isconfigured to access example intent data 408 from the intents. In thisexample, the intent data 408 includes at least one of a uniform resourceindicator (URI) passed with the intent, user actions within theapplication, or media data including a title of the media and/orcaptured video frames from the media.

In the illustrated example, the data collector 208 of FIG. 2 collectsthe intent data 408 based on the second meter. In this example, themapping controller 210 of FIG. 2 can select an example second mapping410 to map the intent data 408 to the example outputs 304. For example,the mapping controller 210 can use the second mapping 410 to generatethe metadata 304A based on the URI and the media data passed with theintent. In some examples, the mapping controller 210 can determine thestate information 304B based on the user actions passed with the intent.In some examples, the metadata 304A and the state information 304B canbe stored in the database 218 of FIG. 2, or sent to the data processingfacility 110 of FIG. 1 via the network 108 of FIG. 1.

FIG. 5A is a block diagram of a fourth example process flow 500 that canbe implemented by the example adaptive metering controller 106 of FIG. 2utilizing custom firmware. For example, a user of the media device 104of FIG. 1 can install the custom firmware onto the media device 104 toenable data collection by the adaptive metering controller 106. In someexamples, the custom firmware enables data to be collected fromthird-party applications. In this example, the meter selector 206 ofFIG. 2 selects a third meter for collecting data pertaining to the mediadevice 104, where the third meter utilizes the custom firmware of themedia device 104.

In this example, the data collector 208 of FIG. 2 collects examplecustom firmware data 502 based on the third meter. In some examples, thecustom firmware data 502 includes at least one of data from memoryand/or storage of the media device 104, audio data, video data, imagedata, and/or network traffic data. In some such examples, the customfirmware data 502 is associated with media presented by the third-partyapplications installed on the media device 104. In this example, themapping controller 210 of FIG. 2 selects an example third mapping 504 tomap the custom firmware data 502 to the example outputs 304. Forexample, the mapping controller 210 can use the third mapping 504 togenerate the metadata 304A and the state information 304B based on thecustom firmware data 502. In some examples, the data processor 212 canprocess the custom firmware data 502 prior to mapping, where theprocessing of the custom firmware data 502 reduces an amount of the datainput to the third mapping 504 and, thus, reduces computational load onthe adaptive metering controller 106.

FIG. 5B is a block diagram of a fifth example process flow 506 that canbe implemented by the example adaptive metering controller 106 of FIG. 2utilizing external devices. In this example, the external devicesinclude a camera and/or a microphone of the media device 104 of FIG. 1.In this example, the meter selector 206 of FIG. 2 selects a fourth meterfor collecting data pertaining to the media device 104, where the fourthmeter utilizes the external devices of the media device 104.

In this example, the data collector 208 of FIG. 2 collects exampleexternal device data 508 based on the fourth meter. In some examples,the external device data 508 includes at least one audio data capturedvia the microphone, video data captured via the camera, image datacaptured via the camera, and/or network traffic data. In some suchexamples, the external device data 508 is associated with mediapresented by an application installed on the media device 104. In thisexample, the mapping controller 210 of FIG. 2 can select an examplefourth mapping 510 to map the external device data 508 to the exampleoutputs 304. For example, the mapping controller 210 can use the fourthmapping 510 to generate the metadata 304A and the state information 304Bbased on the external device data 508. In some examples, the dataprocessor 212 can process the external device data 508 prior to mapping,where the processing of the external device data 508 reduces an amountof the data being input to the fourth mapping 510 to reducecomputational load on the adaptive metering controller 106.

FIG. 6A is a block diagram of a sixth example process flow 600 that canbe implemented by the example adaptive metering controller 106 of FIG. 2utilizing network traffic. In this example, example network traffic data602 is associated with Hypertext Transfer Protocol (HTTP) traffic on avirtual private network (VPN). In this example, the meter selector 206of FIG. 2 selects a fifth meter for collecting data pertaining to themedia device 104, where the fifth meter utilizes the network trafficacross the VPN.

In this example, the data collector 208 of FIG. 2 collects the networktraffic data 602 based on the fifth meter. In some examples, a subset ofthe network traffic data 602 is associated with media presented by oneor more third-party applications installed on the media device 104. Insome examples, the data collector 208 uses artificial intelligence (AI)and/or machine learning (ML) techniques to develop a trained model toselect the subset of the network traffic data 602 that includesinformation relevant to the outputs 304. In some such examples, themapping controller 210 of FIG. 2 can select an example fifth mapping 604to map the network traffic data 602 and/or the subset of the networktraffic data 602 to the example outputs 304. For example, the mappingcontroller 210 can use the fifth mapping 604 to generate the metadata304A and the state information 304B based on the network traffic data602. In some examples, the mapping controller 210 generates and/ortrains a neural network/ML model to be implemented to generate the fifthmapping 604. In particular, the neural network can be trained based ondata parameters (e.g., accuracy metrics, etc.) associated with theinputs 602 and/or the outputs 304. In other examples, the fifth mapping604 is generated manually or using string comparisons.

FIG. 6B is a block diagram of a seventh example process flow 606 thatcan be implemented by the example adaptive metering controller 106 ofFIG. 2 utilizing user-generated inputs. In this example, the meterselector 206 of FIG. 2 selects a sixth meter for collecting datapertaining to the media device 104, where the sixth meter utilizes theuser-generated inputs.

In this example, a dialog and/or request for input may be presented to auser of the media device 104 when media is played on the media device104. In some examples, the user may be prompted by the dialog to inputinformation pertaining to the media being played. In some examples, thedialog is generated based on the accessibility service of the mediadevice 104 or based on network traffic. In this example, exampleuser-generated data 608 includes the information input by the user inthe dialog. As such, the mapping controller 210 of FIG. 2 can select anexample sixth mapping 610 to map the user-generated data 608 to theexample outputs 304. In some examples, the information input by the usercan include one or more of the outputs 304. In such examples, the datacollector 208 of FIG. 2 can directly (e.g., without the sixth mapping610) obtain the outputs 304 using the fifth meter.

FIG. 7 is a block diagram of an eighth example process flow 700 that canbe implemented by the example adaptive metering controller 106 of FIG. 2to process call and/or SMS (short message service) data. In thisexample, a mobile device (e.g., the media device 104 of FIG. 1) can atleast receive an incoming call, receive an incoming SMS message, send anoutgoing call, and/or send an outgoing SMS message. In one example, inresponse to an example incoming or outgoing call or SMS message 702, themobile device provides call logs to the adaptive metering controller106. In some examples, the meter selector 206 of FIG. 2 selects anexample seventh meter, and the data collector 208 of FIG. 2 can obtainthe call logs using the seventh meter. The call logs may include a callor SMS event based on the incoming or outgoing call or SMS message 702.In such examples, at the call log provision 704, the data processor 212of FIG. 2 can determine the call or SMS events based on the call logs.In this example, at the sending of the call or SMS events 706, the dataprocessor 212 sends the call or SMS events to one or more of the servers112 of the data processing facility 110 of FIG. 1. For example, the dataprocessor 212 can send the call or SMS events to the one or more of theservers 112 via the network 108 of FIG. 1.

Alternatively, in response to the incoming or outgoing call or SMSmessage 702, the mobile device provides call and SMS notifications tothe adaptive metering controller 106. In some examples, the meterselector 206 selects an example eighth meter, and the data collector 208can obtain the call and SMS notifications using the eighth meter. Insuch examples, at the call or SMS event generation 708, the adaptivemetering controller 106 generates the call or SMS events based on thecall and SMS notifications and/or based on data from an applicationclass (e.g., AudioManager, MediaSession, etc.) of the mobile device. Forexample, the mapping controller 210 of FIG. 2 can select an exampleseventh mapping to map the call and SMS notifications to the call or SMSevents. In some examples, in response to the sending of the call or SMSevents 706, the data processor 212 provides, transmits and/or sends thegenerated call or SMS events to one or more of the servers 112. In theillustrated example, the call or SMS events based on the call logs arethe same as or similar to the call or SMS events generated based on thecall and SMS notifications. As such, in some examples, at the processingof the call and SMS events 710, the servers 112 similarly process thecall or SMS events based on the call logs and notifications pertainingto the call and SMS.

FIG. 8 is a block diagram of a ninth example process flow 800 that canbe implemented by the example adaptive metering controller 106 of FIG. 2to process media event data associated with an application 802 of themobile device (e.g., the media device 104 of FIG. 1). In this example,the application 802 is configured to present media to a user of themobile device. In one example, in response to the application 802presenting the media, metadata (e.g., MediaSession metadata) associatedwith the media is available to the adaptive metering controller 106. Insuch an example, the meter selector 206 of FIG. 2 selects an exampleninth meter, and the data collector 208 of FIG. 2 can obtain themetadata using the ninth meter. The metadata may include informationassociated with the presented media, such as a media title, showepisode, content length, etc. In such an example, at the obtaining ofthe metadata 804, the data processor 212 of FIG. 2 can determine themedia event data based on the metadata, where the media event data caninclude the information associated with the presented media (e.g., themedia title, the show episode, the content length, etc.). In thisexample, at the sending of the media events 806, the data processor 212sends the media events to one or more of the servers 112 of the dataprocessing facility 110 of FIG. 1. For example, the data processor 212can send the media events to the one or more of the servers 112 via thenetwork 108 of FIG. 1.

In another example, in response to the application 802 presenting themedia, a portion of the metadata is available to the adaptive meteringcontroller 106. In such an example, the meter selector 206 selects acombination of meters including the first meter of FIG. 4A and the ninthmeter, for example. Accordingly, the data collector 208 of FIG. 2 canobtain the portion of the metadata using the ninth meter, and the datacollector 208 can further obtain the accessibility data 402 of FIG. 4Ausing the first meter. In this example, at the obtaining of theaccessibility data and metadata 808, the mapping controller 210 of FIG.2 can select an example eighth mapping to map both the accessibilitydata 402 and the metadata to the media event data. The data processor212 sends the media events to the one or more of the servers 112 at thesending of the media events 806.

In yet another example, in response to the application 802 presentingthe media, the metadata can be unavailable to the adaptive meteringcontroller 106. In such an example, the meter selector 206 selects thefirst meter, and the data collector 208 obtains the accessibility data402 using the first meter. In this particular example, in response toobtaining the accessibility data 810, the mapping controller 210 selectsthe example first mapping 404 of FIG. 4 to map the accessibility data402 to the media event data. In this example, the data processor 212sends the media events to the one or more of the servers 112 at thesending of the media events 806. In the illustrated example, at or inresponse to the processing of the media events 712, the servers 112similarly process the media events based on the metadata, theaccessibility data 402, and/or a combination of the metadata and theaccessibility data 402, etc.

While an example manner of implementing the adaptive metering controller106 of FIG. 1 is illustrated in FIG. 2, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example condition analyzer 204, the example meter selector206, the example data collector 208, the example mapping controller 210,the example data processor 212, the example meter adjuster 214, and/or,more generally, the example adaptive metering controller 106 of FIG. 2may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example condition analyzer 204, the example meter selector206, the example data collector 208, the example mapping controller 210,the example data processor 212, the example meter adjuster 214 and/or,more generally, the example adaptive metering controller 106 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), programmable controller(s), graphicsprocessing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example condition analyzer 204, the example meter selector 206, theexample data collector 208, the example mapping controller 210, theexample data processor 212, and/or the example meter adjuster 214 is/arehereby expressly defined to include a non-transitory computer readablestorage device or storage disk such as a memory, a digital versatiledisk (DVD), a compact disk (CD), a Blu-ray disk, etc. including thesoftware and/or firmware. Further still, the example adaptive meteringcontroller 106 of FIG. 2 may include one or more elements, processesand/or devices in addition to, or instead of, those illustrated in FIG.4, and/or may include more than one of any or all of the illustratedelements, processes and devices. As used herein, the phrase “incommunication,” including variations thereof, encompasses directcommunication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the adaptive metering controller106 of FIG. 2 is shown in FIG. 9. The machine readable instructions maybe one or more executable programs or portion(s) of an executableprogram for execution by a computer processor and/or processorcircuitry, such as the processor 1012 shown in the example processorplatform 1000 discussed below in connection with FIG. 10. The programmay be embodied in software stored on a non-transitory computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, aBlu-ray disk, or a memory associated with the processor 1012, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 1012 and/or embodied in firmware ordedicated hardware. Further, although the example program is describedwith reference to the flowchart illustrated in FIG. 9, many othermethods of implementing the example adaptive metering controller 106 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware. The processor circuitry may bedistributed in different network locations and/or local to one or moredevices (e.g., a multi-core processor in a single machine, multipleprocessors distributed across a server rack, etc).

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as dataor a data structure (e.g., portions of instructions, code,representations of code, etc.) that may be utilized to create,manufacture, and/or produce machine executable instructions. Forexample, the machine readable instructions may be fragmented and storedon one or more storage devices and/or computing devices (e.g., servers)located at the same or different locations of a network or collection ofnetworks (e.g., in the cloud, in edge devices, etc.). The machinereadable instructions may require one or more of installation,modification, adaptation, updating, combining, supplementing,configuring, decryption, decompression, unpacking, distribution,reassignment, compilation, etc. in order to make them directly readable,interpretable, and/or executable by a computing device and/or othermachine. For example, the machine readable instructions may be stored inmultiple parts, which are individually compressed, encrypted, and storedon separate computing devices, wherein the parts when decrypted,decompressed, and combined form a set of executable instructions thatimplement one or more functions that may together form a program such asthat described herein.

In another example, the machine readable instructions may be stored in astate in which they may be read by processor circuitry, but requireaddition of a library (e.g., a dynamic link library (DLL)), a softwaredevelopment kit (SDK), an application programming interface (API), etc.in order to execute the instructions on a particular computing device orother device. In another example, the machine readable instructions mayneed to be configured (e.g., settings stored, data input, networkaddresses recorded, etc.) before the machine readable instructionsand/or the corresponding program(s) can be executed in whole or in part.Thus, machine readable media, as used herein, may include machinereadable instructions and/or program(s) regardless of the particularformat or state of the machine readable instructions and/or program(s)when stored or otherwise at rest or in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example process of FIG. 9 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIG. 9 is a flowchart representative of machine readable instructions900 which may be executed to implement examples disclosed herein. Forexample, the instructions 900 can be executed by the example adaptivemetering controller 106 of FIG. 2 to collect data pertaining to a mobiledevice (e.g., the media device 104 of FIG. 1) using one or more meters.The instructions 900 begin as the adaptive metering controller 106interfaces with the mobile device via the example data interface 216 ofFIG. 2.

At block 902, the example adaptive metering controller 106 determines acondition associated with the mobile device. For example, the conditionanalyzer 204 of FIG. 2 determines the condition including at least oneof a desired accuracy of the data to be monitored, a panel associatedwith the mobile device, or an availability of the one or more meters.

At block 904, the example adaptive meter controller 106 selects a meterfrom the one or more meters. For example, the example meter selector 206of FIG. 2 selects the meter for the mobile device based on thecondition. In some examples, the example meter selector 206 selects acombination of meters from the one or more meters based on thecondition.

At block 906, the example adaptive meter controller 106 determineswhether to generate a mapping. For example, in response to the examplemapping controller 210 of FIG. 2 determining that the mapping is to begenerated (e.g., block 906 returns a result of YES), the processproceeds to block 908. Alternatively, in response to the mappingcontroller 210 determining that the mapping is not to be generated(e.g., block 906 returns a result of NO), the process proceeds to block910. In some examples, the mapping controller 210 determines that themapping is to be generated in response to the meter selector 206selecting the fifth meter, where the fifth meter corresponds to thenetwork traffic data 602 of FIG. 6A.

At block 908, the example adaptive meter controller 106 generates themapping. For example, the example mapping controller 210 generates themapping based on a neural network model. In some examples, the mappingcontroller 210 stores the generated mapping in the example database 218of FIG. 2.

At block 910, the example adaptive metering controller 106 selects themapping from one or more generated mappings. For example, the mappingcontroller 210 selects the mapping based on the selected meter. In someexamples, the mapping controller 210 selects the mapping from theexample mappings 404, 410, 504, 510, 604, 610 described above inconnection with FIGS. 4A, 4B, 5A, 5B, 6A, and/or 6B. In some examples,the mapping controller 210 selects a combination of mappings from themappings 404, 410, 504, 510, 604, 610, where the combination of mappingscorresponds to the combination of meters selected by the meter selector206.

At block 912, the example adaptive metering controller 106 collects datapertaining to the mobile device. For example, the example data collector208 of FIG. 2 collects the data based on the selected meter. In someexamples, the collected data is associated with media presented at themobile device. In some examples, the data collector 208 stores thecollected data in the database 218.

At block 914, the example adaptive metering controller 106 determineswhether the collected data satisfies the condition. For example, theexample meter adjuster 214 of FIG. 2 determines whether the collecteddata is at the desired accuracy. In response to the example meteradjuster 214 determining that the collected data satisfies the condition(e.g., block 914 returns a result of YES), the process proceeds to block916. Alternatively, in response to the example meter adjuster 214determining that the collected data does not satisfy the condition(e.g., block 914 returns a result of NO), the process returns to block902.

At block 916, the example adaptive metering controller 106 organizesand/or processes the collected data. For example, the data processor 212obtains the example outputs 304 of FIG. 3 from the collected data, andsends the collected data and/or the example outputs 304 to the dataprocessing facility 110 of FIG. 1 for further processing. In otherexamples, the mapping controller 210 uses the selected and/or generatedmapping to generate the outputs 304 based on the collected data.

At block 918, the example adaptive metering controller 106 determineswhether to repeat the process. For example, the condition analyzer 204determines whether more data pertaining to the mobile device is to becollected. In response to the condition analyzer 204 determining thatthe process is to be repeated (e.g., block 918 returns a result of YES),the process returns to block 902. Alternatively, in response to thecondition analyzer 204 determining that the process is to be repeated(e.g., block 918 returns a result of NO), the process ends.

FIG. 10 is a block diagram of an example processor platform 1000structured to execute the instructions of FIG. 9 to implement theadaptive metering controller 106 of FIG. 2. The processor platform 1000can be, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad), a personal digitalassistant (PDA), an Internet appliance, a DVD player, a CD player, adigital video recorder, a Blu-ray player, a gaming console, a personalvideo recorder, a set top box, a headset or other wearable device, orany other type of computing device.

The processor platform 1000 of the illustrated example includes aprocessor 1012. The processor 1012 of the illustrated example ishardware. For example, the processor 1012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example condition analyzer204, the example meter selector 206, the example data collector 208, theexample mapping controller 210, the example data processor 212, and theexample meter adjuster 214.

The processor 1012 of the illustrated example includes a local memory1013 (e.g., a cache). The processor 1012 of the illustrated example isin communication with a main memory including a volatile memory 1014 anda non-volatile memory 1016 via a bus 1018. The volatile memory 1014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1016 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1014,1016 is controlled by a memory controller.

The processor platform 1000 of the illustrated example also includes aninterface circuit 1020. The interface circuit 1020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1022 are connectedto the interface circuit 1020. The input device(s) 1022 permit(s) a userto enter data and/or commands into the processor 1012. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1024 are also connected to the interfacecircuit 1020 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1020 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1026. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1000 of the illustrated example also includes oneor more mass storage devices 1028 for storing software and/or data.Examples of such mass storage devices 1028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 1032 of FIG. 9 may be stored in themass storage device 1028, in the volatile memory 1014, in thenon-volatile memory 1016, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that provideadaptive metering of data pertaining to a mobile device. Examplesdisclosed herein enable an adaptive metering controller to adaptivelyconfigure the number and/or type of meters used for collecting data. Thedisclosed methods, apparatus and articles of manufacture improve theefficiency of using a computing device by enabling continuous (e.g.,without interruption) monitoring of media content on the mobile devicewhile ensuring that the collected data satisfies a desired accuracy. Thedisclosed methods, apparatus and articles of manufacture are accordinglydirected to one or more improvement(s) in the functioning of a computer.

Example 1 includes an apparatus including a condition analyzer todetermine a condition associated with a mobile device, a meter selectorto select a meter for the mobile device based on the condition, and adata collector to collect data pertaining to the mobile device based onthe selected meter.

Example 2 includes the apparatus of Example 1, and further includes amapping controller to map the data to one or more outputs, the one ormore outputs associated with media presented on the mobile device.

Example 3 includes the apparatus of Example 2, where the mappingcontroller is to map the data to the one or more outputs via at leastone of a machine learning model or a neural network model.

Example 4 includes the apparatus of Example 2, where the one or moreoutputs identify at least one of a user, a show, a season, an episode, astart time, an end time, a media type, an audio language, or a playerassociated with the media.

Example 5 includes the apparatus of Example 1, where the data iscollected using at least one of an accessibility service, intentfilters, firmware, an external device associated with the mobile device,user-generated inputs, or network traffic.

Example 6 includes the apparatus of Example 5, where the external deviceincludes at least one of a video or a microphone, the data to include atleast one of audio data, video data, or image data.

Example 7 includes the apparatus of Example 1, where the conditionincludes at least one of a desired accuracy of the data or a panelassociated with the mobile device.

Example 8 includes the apparatus of Example 1, where the data isassociated with at least one of an incoming call to the mobile device,an outgoing call from the mobile device, an incoming SMS message to themobile device, or an outgoing SMS message from the mobile device.

Example 9 includes the apparatus of Example 1, where the data isassociated with media presented by an application of the mobile device.

Example 10 includes an apparatus including a memory storing instructionsand a processor to execute the instructions to determine a conditionassociated with a mobile device, select a meter for the mobile devicebased on the condition, and collect data pertaining to the mobile devicebased on the selected meter.

Example 11 includes the apparatus of Example 10, where the processor isto map the data to one or more outputs, the one or more outputsassociated with media presented on the mobile device.

Example 12 includes the apparatus of Example 11, where the processor isto map the data to the one or more outputs by mapping the data via atleast one of a machine learning model or a neural network model.

Example 13 includes the apparatus of Example 11, where the one or moreoutputs identify at least one of a user, a show, a season, an episode, astart time, an end time, a media type, an audio language, or a playerassociated with the media.

Example 14 includes the apparatus of Example 10, where the processor isto collect the data by collecting the data using at least one of anaccessibility service, intent filters, firmware, an external deviceassociated with the mobile device, user-generated inputs, or networktraffic.

Example 15 includes the apparatus of Example 14, where the externaldevice includes at least one of a video or a microphone, the data toinclude at least one of audio data, video data, or image data.

Example 16 includes the apparatus of Example 10, where the processor isto determine the condition by determining at least one of a desiredaccuracy of the data or a panel associated with the mobile device.

Example 17 includes a non-transitory computer readable medium includinginstructions that, when executed, cause at least one processor todetermine a condition associated with a mobile device, select a meterfor the mobile device based on the condition, and collect datapertaining to the mobile device based on the selected meter.

Example 18 includes the non-transitory computer readable medium ofExample 17, where the instructions, when executed, cause the at leastone processor to map the data to one or more outputs, the one or moreoutputs associated with media presented on the mobile device.

Example 19 includes the non-transitory computer readable medium ofExample 18, where the instructions, when executed, cause the at leastone processor to map the data to the one or more outputs via at leastone of a machine learning model or a neural network model.

Example 20 includes the non-transitory computer readable medium ofExample 18, where the instructions, when executed, cause the at leastone processor to identify at least one of a user, a show, a season, anepisode, a start time, an end time, a media type, an audio language, ora player associated with the media.

Example 21 includes the non-transitory computer readable medium ofExample 17, where the instructions, when executed, cause the at leastone processor to collect the data using at least one of an accessibilityservice, intent filters, firmware, an external device associated withthe mobile device, user-generated inputs, or network traffic.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

1. An apparatus comprising: memory; instructions; and processorcircuitry to execute the instructions to at least: determine an accuracythreshold of data to be collected from a mobile device; select a numberof meters and meter types for one or more meters to be used with themobile device based on the accuracy threshold; and collect the datapertaining to the mobile device based on the selected number of metersand the selected meter types.
 2. The apparatus of claim 1, wherein theprocessor circuitry is to execute the instructions to map the data toone or more outputs, the one or more outputs associated with mediapresented on the mobile device.
 3. The apparatus of claim 2, wherein theprocessor circuitry is to execute the instructions to map the data tothe one or more outputs via at least one of a machine learning model ora neural network model.
 4. The apparatus of claim 2, wherein the one ormore outputs identify at least one of a user, a show, a season, anepisode, a start time, an end time, a media type, an audio language, ora player associated with the media.
 5. The apparatus of claim 1, whereinthe processor circuitry is to execute the instructions to collect thedata with at least one of an accessibility service, intent filters,firmware, an external device associated with the mobile device,user-generated inputs, or network traffic.
 6. The apparatus of claim 5,wherein the processor circuitry is to execute the instructions tocollect the data with the external device, the external device includingat least one of a camera or a microphone, the data to include at leastone of audio data, video data, or image data.
 7. The apparatus of claim1, wherein the processor circuitry is to execute the instructions todetermine a panel to which a user of the mobile device belongs, theprocessor circuitry to select the number of meters and the meter typesbased on the panel.
 8. The apparatus of claim 1, wherein the data isassociated with at least one of an incoming call to the mobile device,an outgoing call from the mobile device, an incoming SMS message to themobile device, or an outgoing SMS message from the mobile device.
 9. Theapparatus of claim 1, wherein the data is associated with mediapresented by an application of the mobile device.
 10. An apparatuscomprising: a memory storing instructions; and a processor to executethe instructions to: determine an accuracy threshold of data to becollected from a mobile device; select a number of meters and metertypes for one or more meters to be used with the mobile device based onthe accuracy threshold; and collect the data pertaining to the mobiledevice based on the selected number of meters and the selected metertypes.
 11. The apparatus of claim 10, wherein the processor is toexecute the instructions to map the data to one or more outputs, the oneor more outputs associated with media presented on the mobile device.12. The apparatus of claim 11, wherein the processor is to execute theinstructions to map the data to the one or more outputs by mapping thedata via at least one of a machine learning model or a neural networkmodel.
 13. The apparatus of claim 11, wherein the one or more outputsidentify at least one of a user, a show, a season, an episode, a starttime, an end time, a media type, an audio language, or a playerassociated with the media.
 14. The apparatus of claim 10, wherein theprocessor is to execute the instructions to collect the data bycollecting the data with at least one of an accessibility service,intent filters, firmware, an external device associated with the mobiledevice, user-generated inputs, or network traffic.
 15. The apparatus ofclaim 14, wherein the external device includes at least one of a cameraor a microphone, the data to include at least one of audio data, videodata, or image data.
 16. The apparatus of claim 10, wherein theprocessor is to execute the instructions to determine a panel to which auser of the mobile device belongs, the processor to select the number ofmeters and the meter types based on the panel.
 17. A non-transitorycomputer readable medium comprising instructions that, when executed,cause at least one processor to: determine an accuracy threshold of datato be collected from a mobile device; select a number of meters andmeter types for one or more meters to be used with the mobile devicebased on the accuracy threshold; and collect the data pertaining to themobile device based on the selected number of meters and the selectedmeter types.
 18. The non-transitory computer readable medium of claim17, wherein the instructions, when executed, cause the at least oneprocessor to map the data to one or more outputs, the one or moreoutputs associated with media presented on the mobile device.
 19. Thenon-transitory computer readable medium of claim 18, wherein theinstructions, when executed, cause the at least one processor to map thedata to the one or more outputs via at least one of a machine learningmodel or a neural network model.
 20. The non-transitory computerreadable medium of claim 18, wherein the instructions, when executed,cause the at least one processor to identify at least one of a user, ashow, a season, an episode, a start time, an end time, a media type, anaudio language, or a player associated with the media.
 21. Thenon-transitory computer readable medium of claim 17, wherein theinstructions, when executed, cause the at least one processor to collectthe data with at least one of an accessibility service, intent filters,firmware, an external device associated with the mobile device,user-generated inputs, or network traffic.